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Remote Sensing
Volume 17
Issue 22
10.3390/rs17223719
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Improved Atmospheric Correction for Remote Imaging Spectroscopy Missions with Accelerated Optimal Estimation

by
Jouni Susiluoto
*,
Niklas Bohn
,
Amy Braverman
,
Philip G. Brodrick
,
Nimrod Carmon
,
Michael R. Gunson
,
Hai Nguyen
,
David R. Thompson
and
Michael Turmon
Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(22), 3719; https://doi.org/10.3390/rs17223719
Submission received: 16 December 2024 / Revised: 1 February 2025 / Accepted: 12 February 2025 / Published: 14 November 2025
(This article belongs to the Topic Hyperspectral Imaging and Signal Processing)
Versions Notes

Abstract

Space-based imaging spectrometers that monitor the Earth’s surface generate vast amounts of data, the processing of which requires fast and accurate retrieval algorithms. Estimating scientifically relevant surface properties from remotely measured radiance data typically involves first inferring spectral surface reflectance from the observed radiance, followed by discipline-specific algorithms to derive scientifically relevant properties. Probabilistic reflectance retrieval algorithms, such as the commonly used optimal estimation (OE), are computationally expensive. Furthermore, the Gaussian assumptions associated with OE have not been fully validated in the context of hyperspectral retrievals. To address these challenges, we introduce accelerated optimal estimation (AOE), a Bayesian algorithm that speeds up the OE reflectance inversion process by up to two orders of magnitude compared to a reference OE implementation (ROE), while also providing improved convergence over a number of selected test targets. We also demonstrate that, under given atmospheric conditions, Gaussian uncertainty estimates from OE-type algorithms are accurate. This is achieved by comparing the OE-type posterior distributions to non-Gaussian ones obtained with Markov chain Monte Carlo (MCMC). Finally, we demonstrate how AOE scales to a larger AVIRIS-NG scene, showcasing its ability to handle complex, large-scale data.
Keywords: imaging spectroscopy; optimal estimation; atmospheric correction imaging spectroscopy; optimal estimation; atmospheric correction

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MDPI and ACS Style

Susiluoto, J.; Bohn, N.; Braverman, A.; Brodrick, P.G.; Carmon, N.; Gunson, M.R.; Nguyen, H.; Thompson, D.R.; Turmon, M. Improved Atmospheric Correction for Remote Imaging Spectroscopy Missions with Accelerated Optimal Estimation. Remote Sens. 2025, 17, 3719. https://doi.org/10.3390/rs17223719

AMA Style

Susiluoto J, Bohn N, Braverman A, Brodrick PG, Carmon N, Gunson MR, Nguyen H, Thompson DR, Turmon M. Improved Atmospheric Correction for Remote Imaging Spectroscopy Missions with Accelerated Optimal Estimation. Remote Sensing. 2025; 17(22):3719. https://doi.org/10.3390/rs17223719

Chicago/Turabian Style

Susiluoto, Jouni, Niklas Bohn, Amy Braverman, Philip G. Brodrick, Nimrod Carmon, Michael R. Gunson, Hai Nguyen, David R. Thompson, and Michael Turmon. 2025. "Improved Atmospheric Correction for Remote Imaging Spectroscopy Missions with Accelerated Optimal Estimation" Remote Sensing 17, no. 22: 3719. https://doi.org/10.3390/rs17223719

APA Style

Susiluoto, J., Bohn, N., Braverman, A., Brodrick, P. G., Carmon, N., Gunson, M. R., Nguyen, H., Thompson, D. R., & Turmon, M. (2025). Improved Atmospheric Correction for Remote Imaging Spectroscopy Missions with Accelerated Optimal Estimation. Remote Sensing, 17(22), 3719. https://doi.org/10.3390/rs17223719

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